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## SKILL.md

      
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                tandpfun
                / SKILL.md
            
            
              Created
              July 14, 2026 02:16
            
              
                Extract Clothing Skill
              
          
    name
    extract-clothing-cutouts
  
  
    description
    Extract high-quality, deduplicated transparent ecommerce clothing cutouts from a folder of photographs where people wear one or more garments. Use when Codex must find outfit or model photos, identify unique clothing across images, create focused references, reconstruct complete garments with Imagegen, remove a solid chroma background into RGBA PNGs, and output only the finished clothing images into a new folder under the current working directory.
  
  
Extract Clothing Cutouts

Turn photographs of worn clothing into source-faithful standalone catalog PNGs. Treat each result as a reconstruction from visible evidence, not literal segmentation whenever the wearer or another layer occludes part of the garment.
Start by asking for two paths


## Export-MFP-data.js
// ==UserScript==
// @name         MyFitnessPal Full Export → ZIP (API Monthly + Weight)
// @namespace    http://github.com/1337-server
// @version      2.7
// @description  Fetch monthly diary via API, store month JSONs, extract per-day JSONs (gap-filled), and fetch 1-year weight report (normalized dates) into weight.json
// @author       1337-server
// @license      MIT
// @match        https://www.myfitnesspal.com/*
// @grant        GM_addStyle
// @downloadURL  https://update.greasyfork.org/scripts/554485/MyFitnessPal%20Full%20Export%20%E2%86%92%20ZIP%20%28API%20Monthly%20%2B%20Weight%29.user.js

## gist:b4e22cb3ac7782a1c43d5324a0413505
Set up these as real Codex lifecycle hooks by following the official documentation:
https://learn.chatgpt.com/docs/hooks

Important: use ~/.codex/hooks.json and a PreToolUse command hook. Do not implement this only as a ~/.codex/rules/*.rules execution-policy file.

First inspect any existing hooks and preserve them. Then:

1. Create or merge ~/.codex/hooks.json with a PreToolUse matcher for Bash.
2. Create ~/.codex/hooks/destructive_commands.py.
3. The script must read the hook JSON payload from stdin and inspect tool_input.command.

## llm-wiki.md

      
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                karpathy
                / llm-wiki.md
            
            
              Created
              April 4, 2026 16:25
            
              
                llm-wiki
              
          
    LLM Wiki

A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

  
## GraphicsAPIs.md

      
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                MangaD
                / GraphicsAPIs.md
            
            
              Last active
              July 14, 2026 19:56
            
              
                The Comprehensive Guide to Graphics APIs: From Historical Roots to Modern Applications and Beyond
              
          
    The Complete Guide to Graphics APIs: History, Architecture, OpenGL, Vulkan, DirectX, Metal, WebGPU, Zink, Translation Layers, and the Future of GPU Programming


Disclaimer: ChatGPT generated document.
Introduction

Graphics APIs are among the most important, yet often misunderstood, parts of modern computing.

  
## thread-pm-skill.md

      
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              8 stars
            
          
                hashimwarren
                / thread-pm-skill.md
            
            
              Last active
              July 14, 2026 19:53
            
              
                Product Manager skill for orchestrating independent Codex app threads
              
          
    name
    thread-pm
  
  
    description
    Product Manager for orchestrating independent Codex app threads. Prioritizes work with RICE, allocates compute by risk-adjusted expected value, forecasts budgets, routes models and reasoning, defines goals, monitors execution, steers threads, and synthesizes results. Never uses subagents unless the user explicitly authorizes them.
  
  
Thread PM

Role

Act as a Product Manager with expertise in Codex app capabilities and OpenAI models.
	// ==UserScript==
	// @name MyFitnessPal Full Export → ZIP (API Monthly + Weight)
	// @namespace http://github.com/1337-server
	// @version 2.7
	// @description Fetch monthly diary via API, store month JSONs, extract per-day JSONs (gap-filled), and fetch 1-year weight report (normalized dates) into weight.json
	// @author 1337-server
	// @license MIT
	// @match https://www.myfitnesspal.com/*
	// @grant GM_addStyle
	// @downloadURL https://update.greasyfork.org/scripts/554485/MyFitnessPal%20Full%20Export%20%E2%86%92%20ZIP%20%28API%20Monthly%20%2B%20Weight%29.user.js
	Set up these as real Codex lifecycle hooks by following the official documentation:
	https://learn.chatgpt.com/docs/hooks

	Important: use ~/.codex/hooks.json and a PreToolUse command hook. Do not implement this only as a ~/.codex/rules/*.rules execution-policy file.

	First inspect any existing hooks and preserve them. Then:

	1. Create or merge ~/.codex/hooks.json with a PreToolUse matcher for Bash.
	2. Create ~/.codex/hooks/destructive_commands.py.
	3. The script must read the hook JSON payload from stdin and inspect tool_input.command.
name	thread-pm
description	Product Manager for orchestrating independent Codex app threads. Prioritizes work with RICE, allocates compute by risk-adjusted expected value, forecasts budgets, routes models and reasoning, defines goals, monitors execution, steers threads, and synthesizes results. Never uses subagents unless the user explicitly authorizes them.